Search results for "normalized convolution"

showing 3 items of 3 documents

Estimating Missing Information by Cluster Analysis and Normalized Convolution

2018

International audience; Smart city deals with the improvement of their citizens' quality of life. Numerous ad-hoc sensors need to be deployed to know humans' activities as well as the conditions in which these actions take place. Even if these sensors are cheaper and cheaper, their installation and maintenance cost increases rapidly with their number. We propose a methodology to limit the number of sensors to deploy by using a standard clustering technique and the normalized convolution to estimate environmental information whereas sensors are actually missing. In spite of its simplicity, our methodology lets us provide accurate assesses.

010504 meteorology & atmospheric sciencesComputer sciencemedia_common.quotation_subjectReal-time computingEnergy Engineering and Power Technology02 engineering and technologyIterative reconstructionsmart city dealsCluster (spacecraft)01 natural sciencesIndustrial and Manufacturing Engineeringnormalized convolutionstandard clustering technique[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]ConvolutionArtificial IntelligenceSmart city11. Sustainability0202 electrical engineering electronic engineering information engineeringLimit (mathematics)SimplicityCluster analysisInstrumentationad-hoc sensors0105 earth and related environmental sciencesmedia_commonSettore INF/01 - InformaticaRenewable Energy Sustainability and the EnvironmentComputer Science Applications1707 Computer Vision and Pattern Recognitionenvironmental informationmissing informationComputer Networks and CommunicationKernel (image processing)020201 artificial intelligence & image processingcluster analysis2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)
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Combining fuzzy C-mean and normalized convolution for cloud detection in IR images

2009

An important task for the cloud monitoring in several frameworks is providing maps of the cloud coverage. In this paper we present a method to detect cloudy pixels for images taken from ground by an infra-red camera. The method is a three-steps algorithm mainly based on a Fuzzy C-Mean clustering, that works on a feature space derived from the original image and the output of the reconstructed image obtained via normalized convolution. Experiments, run on several infra-red images acquired under different conditions, show that the cloud maps returned are satisfactory. © 2009 Springer Berlin Heidelberg.

Infra-red imagePixelSettore INF/01 - InformaticaComputer sciencebusiness.industryFeature vectorFuzzy setComputer Science (all)ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCloud computingFuzzy logicImage (mathematics)Theoretical Computer ScienceNormalized convolutionComputer Science::Computer Vision and Pattern RecognitionFuzzy setComputer visionCloudiness maskArtificial intelligenceCluster analysisbusinessAstrophysics::Galaxy Astrophysics
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Genetic Normalized Convolution

2011

Normalized convolution techniques operate on very few samples of a given digital signal and add missing information, trough spatial interpolation. From a practical viewpoint, they make use of data really available and approximate the assumed values of the missing information. The quality of the final result is generally better than that obtained by traditional filling methods as, for example, bilinear or bicubic interpolations. Usually, the position of the samples is assumed to be random and due to transmission errors of the signal. Vice versa, we want to apply normalized convolution to compress data. In this case, we need to arrange a higher density of samples in proximity of zones which c…

Phase congruencyCorrectnessSettore INF/01 - InformaticaPosition (vector)Genetic algorithmGenetic Algorithms Normalized Convolution Symmetry Transform Structural Similarity Metrics Phase CongruencyBicubic interpolationBilinear interpolationDigital signal (signal processing)AlgorithmMathematicsMultivariate interpolation
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